Recensement
Contents
Recensement¶
Dataviz sur les données du recensement 2019 avec recoupement sur les logements
!pip install pandas seaborn dataprep
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Requirement already satisfied: soupsieve>1.2 in /opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages (from beautifulsoup4->nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (2.3.2.post1)
Requirement already satisfied: webencodings in /opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages (from bleach->nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (0.5.1)
Requirement already satisfied: pycparser in /opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (2.21)
WARNING: There was an error checking the latest version of pip.
!wget "https://data.gouv.nc/explore/dataset/rp-2019-indv-psud/download/?format=csv&timezone=Pacific/Noumea&lang=fr&use_labels_for_header=true&csv_separator=%2C" -O data/recensement_individus.csv
--2022-05-24 05:26:13-- https://data.gouv.nc/explore/dataset/rp-2019-indv-psud/download/?format=csv&timezone=Pacific/Noumea&lang=fr&use_labels_for_header=true&csv_separator=%2C
Resolving data.gouv.nc (data.gouv.nc)... 13.55.171.246, 13.211.119.48
Connecting to data.gouv.nc (data.gouv.nc)|13.55.171.246|:443...
connected.
HTTP request sent, awaiting response...
200 OK
Length: unspecified [application/csv]
Saving to: ‘data/recensement_individus.csv’
data/rece [<=> ] 0 --.-KB/s
data/recen [ <=> ] 36.68K 179KB/s
data/recens [ <=> ] 100.68K 245KB/s
data/recense [ <=> ] 132.67K 188KB/s
data/recensem [ <=> ] 180.66K 185KB/s
data/recenseme [ <=> ] 244.65K 195KB/s
data/recensemen [ <=> ] 260.64K 171KB/s
data/recensement [ <=> ] 308.64K 169KB/s
data/recensement_ [ <=> ] 372.62K 177KB/s
data/recensement_i [ <=> ] 404.61K 166KB/s
data/recensement_in [ <=> ] 436.60K 159KB/s
ata/recensement_ind [ <=> ] 500.59K 164KB/s
ta/recensement_indi [ <=> ] 532.58K 162KB/s
a/recensement_indiv [ <=> ] 596.57K 163KB/s
/recensement_indivi [ <=> ] 676.55K 155KB/s
recensement_individ [ <=> ] 756.53K 165KB/s
ecensement_individu [ <=> ] 764.55K 155KB/s
censement_individus [ <=>] 796.55K 155KB/s
ensement_individus. [ <=> ] 860.55K 156KB/s
nsement_individus.c [ <=> ] 876.55K 152KB/s
sement_individus.cs [ <=> ] 924.55K 152KB/s
ement_individus.csv [ <=> ] 988.55K 150KB/s
ment_individus.csv [ <=> ] 1005K 148KB/s
ent_individus.csv [ <=> ] 1.03M 147KB/s
nt_individus.csv [ <=> ] 1.09M 147KB/s
t_individus.csv [ <=> ] 1.11M 146KB/s
_individus.csv [ <=> ] 1.15M 146KB/s
individus.csv [ <=> ] 1.21M 147KB/s
ndividus.csv [ <=> ] 1.23M 143KB/s
dividus.csv [ <=> ] 1.34M 150KB/s
ividus.csv [ <=> ] 1.43M 149KB/s
vidus.csv [ <=> ] 1.46M 147KB/s
idus.csv [ <=> ] 1.50M 144KB/s
dus.csv [ <=> ] 1.56M 153KB/s
us.csv [<=> ] 1.59M 147KB/s
s.csv [ <=> ] 1.62M 145KB/s
.csv [ <=> ] 1.68M 148KB/s
csv [ <=> ] 1.70M 143KB/s
sv [ <=> ] 1.75M 145KB/s
v [ <=> ] 1.82M 141KB/s
[ <=> ] 1.93M 147KB/s
d [ <=> ] 1.96M 143KB/s
da [ <=> ] 2.00M 144KB/s
dat [ <=> ] 2.06M 146KB/s
data [ <=> ] 2.09M 139KB/s
data/ [ <=> ] 2.15M 149KB/s
data/r [ <=> ] 2.17M 140KB/s
data/re [ <=> ] 2.21M 140KB/s
data/rec [ <=> ] 2.28M 143KB/s
data/rece [ <=> ] 2.29M 140KB/s
data/recen [ <=> ] 2.34M 143KB/s
data/recens [ <=>] 2.40M 142KB/s
data/recense [ <=> ] 2.43M 142KB/s
data/recensem [ <=> ] 2.46M 145KB/s
data/recenseme [ <=> ] 2.53M 142KB/s
data/recensemen [ <=> ] 2.54M 142KB/s
data/recensement [ <=> ] 2.59M 143KB/s
data/recensement_ [ <=> ] 2.65M 143KB/s
data/recensement_i [ <=> ] 2.68M 145KB/s
data/recensement_in [ <=> ] 2.71M 142KB/s
ata/recensement_ind [ <=> ] 2.78M 143KB/s
ta/recensement_indi [ <=> ] 2.81M 143KB/s
a/recensement_indiv [ <=> ] 2.87M 148KB/s
/recensement_indivi [ <=> ] 2.89M 144KB/s
recensement_individ [ <=> ] 2.93M 145KB/s
ecensement_individu [ <=> ] 3.00M 146KB/s
censement_individus [ <=> ] 3.01M 144KB/s
ensement_individus. [ <=> ] 3.06M 144KB/s
nsement_individus.c [<=> ] 3.12M 143KB/s
sement_individus.cs [ <=> ] 3.14M 143KB/s
ement_individus.csv [ <=> ] 3.18M 140KB/s
ment_individus.csv [ <=> ] 3.25M 142KB/s
ent_individus.csv [ <=> ] 3.26M 138KB/s
nt_individus.csv [ <=> ] 3.31M 141KB/s
t_individus.csv [ <=> ] 3.37M 143KB/s
_individus.csv [ <=> ] 3.40M 145KB/s
individus.csv [ <=> ] 3.43M 137KB/s
ndividus.csv [ <=> ] 3.50M 136KB/s
dividus.csv [ <=> ] 3.53M 135KB/s
ividus.csv [ <=> ] 3.59M 138KB/s
vidus.csv [ <=> ] 3.62M 135KB/s
idus.csv [ <=> ] 3.71M 131KB/s
dus.csv [ <=> ] 3.73M 124KB/s
us.csv [ <=> ] 3.78M 124KB/s
s.csv [ <=> ] 3.83M 128KB/s
.csv [ <=>] 3.87M 123KB/s
csv [ <=> ] 3.90M 126KB/s
sv [ <=> ] 3.96M 128KB/s
v [ <=> ] 4.00M 124KB/s
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da [ <=> ] 4.12M 126KB/s
dat [ <=> ] 4.15M 130KB/s
data [ <=> ] 4.21M 128KB/s
data/ [ <=> ] 4.23M 123KB/s
data/r [ <=> ] 4.31M 130KB/s
data/re [ <=> ] 4.34M 136KB/s
data/rec [ <=> ] 4.37M 129KB/s
data/rece [ <=> ] 4.43M 136KB/s
data/recen [ <=> ] 4.46M 131KB/s
data/recens [ <=> ] 4.50M 131KB/s
data/recense [ <=> ] 4.56M 139KB/s
data/recensem [<=> ] 4.59M 143KB/s
data/recenseme [ <=> ] 4.62M 146KB/s
data/recensemen [ <=> ] 4.68M 147KB/s
data/recensement [ <=> ] 4.71M 146KB/s
data/recensement_ [ <=> ] 4.75M 147KB/s
data/recensement_i [ <=> ] 4.81M 146KB/s
data/recensement_in [ <=> ] 4.84M 147KB/s
ata/recensement_ind [ <=> ] 4.87M 145KB/s
ta/recensement_indi [ <=> ] 4.93M 147KB/s
a/recensement_indiv [ <=> ] 4.96M 146KB/s
/recensement_indivi [ <=> ] 5.03M 150KB/s
recensement_individ [ <=> ] 5.06M 148KB/s
ecensement_individu [ <=> ] 5.15M 144KB/s
censement_individus [ <=> ] 5.18M 145KB/s
ensement_individus. [ <=> ] 5.21M 146KB/s
nsement_individus.c [ <=> ] 5.28M 146KB/s
sement_individus.cs [ <=> ] 5.31M 148KB/s
ement_individus.csv [ <=>] 5.34M 148KB/s
ment_individus.csv [ <=> ] 5.40M 147KB/s
ent_individus.csv [ <=> ] 5.46M 146KB/s
nt_individus.csv [ <=> ] 5.53M 153KB/s
t_individus.csv [ <=> ] 5.56M 146KB/s
_individus.csv [ <=> ] 5.59M 146KB/s
individus.csv [ <=> ] 5.65M 150KB/s
ndividus.csv [ <=> ] 5.68M 147KB/s
dividus.csv [ <=> ] 5.75M 150KB/s
ividus.csv [ <=> ] 5.78M 152KB/s
vidus.csv [ <=> ] 5.81M 143KB/s
idus.csv [ <=> ] 5.87M 152KB/s
dus.csv [ <=> ] 5.90M 145KB/s
us.csv [ <=> ] 6.00M 155KB/s
s.csv [ <=> ] 6.06M 159KB/s
.csv [ <=> ] 6.15M 166KB/s
csv [ <=> ] 6.25M 159KB/s
sv [<=> ] 6.28M 155KB/s
v [ <=> ] 6.31M 155KB/s
[ <=> ] 6.37M 162KB/s
d [ <=> ] 6.43M 164KB/s
da [ <=> ] 6.53M 175KB/s
dat [ <=> ] 6.62M 177KB/s
data [ <=> ] 6.68M 183KB/s
data/ [ <=> ] 6.78M 192KB/s
data/r [ <=> ] 6.87M 197KB/s
data/re [ <=> ] 6.96M 206KB/s
data/rec [ <=> ] 7.03M 206KB/s
data/rece [ <=> ] 7.12M 215KB/s
data/recen [ <=> ] 7.21M 227KB/s
data/recens [ <=> ] 7.28M 224KB/s
data/recense [ <=> ] 7.37M 235KB/s
data/recensem [ <=> ] 7.46M 240KB/s
data/recenseme [ <=> ] 7.50M 234KB/s
data/recensemen [ <=>] 7.59M 241KB/s
data/recensement [ <=> ] 7.65M 239KB/s
data/recensement_ [ <=> ] 7.75M 267KB/s
data/recensement_i [ <=> ] 7.84M 269KB/s
data/recensement_in [ <=> ] 7.90M 271KB/s
ata/recensement_ind [ <=> ] 8.00M 272KB/s
ta/recensement_indi [ <=> ] 8.09M 273KB/s
a/recensement_indiv [ <=> ] 8.18M 272KB/s
/recensement_indivi [ <=> ] 8.25M 273KB/s
recensement_individ [ <=> ] 8.34M 272KB/s
ecensement_individu [ <=> ] 8.43M 271KB/s
censement_individus [ <=> ] 8.50M 264KB/s
ensement_individus. [ <=> ] 8.59M 268KB/s
nsement_individus.c [ <=> ] 8.68M 266KB/s
sement_individus.cs [ <=> ] 8.75M 259KB/s
ement_individus.csv [ <=> ] 8.84M 262KB/s
ment_individus.csv [ <=> ] 8.93M 260KB/s
ent_individus.csv [<=> ] 9.03M 258KB/s
nt_individus.csv [ <=> ] 9.09M 257KB/s
t_individus.csv [ <=> ] 9.18M 259KB/s
_individus.csv [ <=> ] 9.28M 259KB/s
individus.csv [ <=> ] 9.34M 259KB/s
ndividus.csv [ <=> ] 9.43M 259KB/s
dividus.csv [ <=> ] 9.53M 265KB/s
ividus.csv [ <=> ] 9.62M 270KB/s
vidus.csv [ <=> ] 9.68M 265KB/s
idus.csv [ <=> ] 9.78M 260KB/s
dus.csv [ <=> ] 9.87M 261KB/s
us.csv [ <=> ] 9.93M 261KB/s
s.csv [ <=> ] 10.03M 260KB/s
.csv [ <=> ] 10.12M 266KB/s
csv [ <=> ] 10.18M 262KB/s
sv [ <=> ] 10.28M 264KB/s
v [ <=> ] 10.37M 269KB/s
[ <=>] 10.46M 272KB/s
d [ <=> ] 10.53M 269KB/s
da [ <=> ] 10.62M 272KB/s
dat [ <=> ] 10.71M 278KB/s
data [ <=> ] 10.78M 272KB/s
data/ [ <=> ] 10.87M 271KB/s
data/r [ <=> ] 10.96M 276KB/s
data/re [ <=> ] 11.06M 275KB/s
data/rec [ <=> ] 11.12M 269KB/s
data/rece [ <=> ] 11.21M 269KB/s
data/recen [ <=> ] 11.31M 275KB/s
data/recens [ <=> ] 11.37M 276KB/s
data/recense [ <=> ] 11.46M 276KB/s
data/recensem [ <=> ] 11.56M 275KB/s
data/recenseme [ <=> ] 11.62M 269KB/s
data/recensemen [ <=> ] 11.71M 274KB/s
data/recensement [ <=> ] 11.81M 274KB/s
data/recensement_ [<=> ] 11.87M 274KB/s
data/recensement_i [ <=> ] 11.96M 274KB/s
data/recensement_in [ <=> ] 12.06M 276KB/s
ata/recensement_ind [ <=> ] 12.12M 271KB/s
ta/recensement_indi [ <=> ] 12.21M 275KB/s
a/recensement_indiv [ <=> ] 12.31M 274KB/s
/recensement_indivi [ <=> ] 12.37M 267KB/s
recensement_individ [ <=> ] 12.46M 270KB/s
ecensement_individu [ <=> ] 12.56M 266KB/s
censement_individus [ <=> ] 12.65M 264KB/s
ensement_individus. [ <=> ] 12.71M 258KB/s
nsement_individus.c [ <=> ] 12.81M 260KB/s
sement_individus.cs [ <=> ] 12.90M 258KB/s
ement_individus.csv [ <=> ] 12.96M 251KB/s
ment_individus.csv [ <=> ] 13.06M 255KB/s
ent_individus.csv [ <=> ] 13.15M 255KB/s
nt_individus.csv [ <=> ] 13.25M 260KB/s
t_individus.csv [ <=>] 13.31M 255KB/s
_individus.csv [ <=> ] 13.40M 255KB/s
individus.csv [ <=> ] 13.50M 255KB/s
ndividus.csv [ <=> ] 13.56M 255KB/s
dividus.csv [ <=> ] 13.65M 255KB/s
ividus.csv [ <=> ] 13.75M 255KB/s
vidus.csv [ <=> ] 13.84M 260KB/s
idus.csv [ <=> ] 13.90M 256KB/s
dus.csv [ <=> ] 14.00M 257KB/s
us.csv [ <=> ] 14.09M 264KB/s
s.csv [ <=> ] 14.15M 261KB/s
.csv [ <=> ] 14.25M 265KB/s
csv [ <=> ] 14.34M 266KB/s
sv [ <=> ] 14.40M 268KB/s
v [ <=> ] 14.50M 272KB/s
[ <=> ] 14.59M 273KB/s
d [ <=> ] 14.68M 281KB/s
da [<=> ] 14.75M 276KB/s
dat [ <=> ] 14.84M 277KB/s
data [ <=> ] 14.93M 276KB/s
data/ [ <=> ] 15.00M 282KB/s
data/r [ <=> ] 15.09M 281KB/s
data/re [ <=> ] 15.18M 280KB/s
data/rec [ <=> ] 15.25M 275KB/s
data/rece [ <=> ] 15.34M 271KB/s
data/recen [ <=> ] 15.43M 271KB/s
data/recens [ <=> ] 15.50M 271KB/s
data/recense [ <=> ] 15.59M 270KB/s
data/recensem [ <=> ] 15.68M 270KB/s
data/recenseme [ <=> ] 15.78M 273KB/s
data/recensemen [ <=> ] 15.84M 267KB/s
data/recensement [ <=> ] 15.93M 269KB/s
data/recensement_ [ <=> ] 16.03M 274KB/s
data/recensement_i [ <=> ] 16.09M 268KB/s
data/recensement_in [ <=>] 16.18M 267KB/s
ata/recensement_ind [ <=> ] 16.28M 266KB/s
ta/recensement_indi [ <=> ] 16.34M 263KB/s
a/recensement_indiv [ <=> ] 16.43M 256KB/s
/recensement_indivi [ <=> ] 16.53M 259KB/s
recensement_individ [ <=> ] 16.59M 251KB/s
ecensement_individu [ <=> ] 16.68M 247KB/s
censement_individus [ <=> ] 16.78M 254KB/s
ensement_individus. [ <=> ] 16.87M 251KB/s
nsement_individus.c [ <=> ] 16.93M 248KB/s
sement_individus.cs [ <=> ] 17.03M 252KB/s
data/recensement_in [ <=> ] 17.14M 260KB/s in 86s
2022-05-24 05:27:41 (204 KB/s) - ‘data/recensement_individus.csv’ saved [17971968]
!wget "https://data.gouv.nc/explore/dataset/rp-2019-logements/download/?format=csv&timezone=Pacific/Noumea&lang=fr&use_labels_for_header=true&csv_separator=%2C" -O data/recensement_logements.csv
--2022-05-24 05:27:41-- https://data.gouv.nc/explore/dataset/rp-2019-logements/download/?format=csv&timezone=Pacific/Noumea&lang=fr&use_labels_for_header=true&csv_separator=%2C
Resolving data.gouv.nc (data.gouv.nc)... 13.55.171.246, 13.211.119.48
Connecting to data.gouv.nc (data.gouv.nc)|13.55.171.246|:443...
connected.
HTTP request sent, awaiting response...
200 OK
Length: unspecified [application/csv]
Saving to: ‘data/recensement_logements.csv’
data/rece [<=> ] 0 --.-KB/s
data/recen [ <=> ] 36.68K 183KB/s
data/recens [ <=> ] 100.67K 250KB/s
data/recense [ <=> ] 212.65K 352KB/s
data/recensem [ <=> ] 340.62K 395KB/s
data/recenseme [ <=> ] 356.61K 328KB/s
data/recensemen [ <=> ] 468.59K 313KB/s
data/recensement [ <=> ] 548.57K 292KB/s
data/recensement_ [ <=> ] 700.55K 312KB/s
data/recensement_l [ <=> ] 796.55K 305KB/s
data/recensement_lo [ <=> ] 892.55K 300KB/s
ata/recensement_log [ <=> ] 988.55K 295KB/s
ta/recensement_loge [ <=> ] 1.06M 292KB/s
a/recensement_logem [ <=> ] 1.15M 289KB/s
/recensement_logeme [ <=> ] 1.28M 294KB/s
recensement_logemen [ <=> ] 1.37M 290KB/s
ecensement_logement [ <=> ] 1.46M 289KB/s
censement_logements [ <=>] 1.56M 287KB/s
ensement_logements. [ <=> ] 1.65M 290KB/s
nsement_logements.c [ <=> ] 1.75M 288KB/s
sement_logements.cs [ <=> ] 1.87M 284KB/s
ement_logements.csv [ <=> ] 1.96M 275KB/s
ment_logements.csv [ <=> ] 2.06M 282KB/s
ent_logements.csv [ <=> ] 2.15M 270KB/s
nt_logements.csv [ <=> ] 2.25M 276KB/s
t_logements.csv [ <=> ] 2.34M 272KB/s
_logements.csv [ <=> ] 2.43M 273KB/s
logements.csv [ <=> ] 2.56M 278KB/s
ogements.csv [ <=> ] 2.65M 277KB/s
gements.csv [ <=> ] 2.75M 273KB/s
ements.csv [ <=> ] 2.78M 266KB/s
ments.csv [ <=> ] 2.84M 255KB/s
ents.csv [ <=> ] 2.93M 245KB/s
nts.csv [ <=> ] 3.00M 241KB/s
ts.csv [<=> ] 3.03M 235KB/s
s.csv [ <=> ] 3.09M 232KB/s
.csv [ <=> ] 3.15M 220KB/s
csv [ <=> ] 3.18M 207KB/s
sv [ <=> ] 3.25M 201KB/s
v [ <=> ] 3.28M 194KB/s
[ <=> ] 3.34M 192KB/s
d [ <=> ] 3.37M 184KB/s
da [ <=> ] 3.43M 178KB/s
dat [ <=> ] 3.50M 181KB/s
data [ <=> ] 3.53M 170KB/s
data/ [ <=> ] 3.59M 162KB/s
data/r [ <=> ] 3.62M 153KB/s
data/re [ <=> ] 3.68M 155KB/s
data/rec [ <=> ] 3.75M 154KB/s
data/rece [ <=> ] 3.78M 154KB/s
data/recen [ <=> ] 3.84M 154KB/s
data/recens [ <=>] 3.87M 148KB/s
data/recense [ <=> ] 3.93M 154KB/s
data/recensem [ <=> ] 4.00M 153KB/s
data/recenseme [ <=> ] 4.03M 150KB/s
data/recensemen [ <=> ] 4.09M 149KB/s
data/recensement [ <=> ] 4.12M 149KB/s
data/recensement_ [ <=> ] 4.18M 153KB/s
data/recensement_l [ <=> ] 4.21M 149KB/s
data/recensement_lo [ <=> ] 4.28M 153KB/s
ata/recensement_log [ <=> ] 4.34M 153KB/s
ta/recensement_loge [ <=> ] 4.37M 153KB/s
a/recensement_logem [ <=> ] 4.43M 153KB/s
/recensement_logeme [ <=> ] 4.46M 146KB/s
recensement_logemen [ <=> ] 4.53M 149KB/s
ecensement_logement [ <=> ] 4.56M 144KB/s
censement_logements [ <=> ] 4.62M 144KB/s
ensement_logements. [ <=> ] 4.68M 145KB/s
nsement_logements.c [<=> ] 4.71M 139KB/s
sement_logements.cs [ <=> ] 4.78M 144KB/s
ement_logements.csv [ <=> ] 4.81M 138KB/s
ment_logements.csv [ <=> ] 4.87M 141KB/s
ent_logements.csv [ <=> ] 4.90M 136KB/s
nt_logements.csv [ <=> ] 4.96M 135KB/s
t_logements.csv [ <=> ] 5.03M 139KB/s
_logements.csv [ <=> ] 5.06M 135KB/s
logements.csv [ <=> ] 5.12M 138KB/s
ogements.csv [ <=> ] 5.15M 134KB/s
gements.csv [ <=> ] 5.21M 138KB/s
ements.csv [ <=> ] 5.28M 138KB/s
ments.csv [ <=> ] 5.31M 131KB/s
ents.csv [ <=> ] 5.37M 136KB/s
nts.csv [ <=> ] 5.40M 132KB/s
ts.csv [ <=> ] 5.46M 138KB/s
s.csv [ <=> ] 5.50M 135KB/s
.csv [ <=>] 5.56M 138KB/s
csv [ <=> ] 5.62M 141KB/s
sv [ <=> ] 5.65M 136KB/s
v [ <=> ] 5.71M 142KB/s
[ <=> ] 5.75M 137KB/s
d [ <=> ] 5.81M 144KB/s
da [ <=> ] 5.87M 144KB/s
dat [ <=> ] 5.90M 145KB/s
data [ <=> ] 5.96M 146KB/s
data/ [ <=> ] 6.00M 142KB/s
data/r [ <=> ] 6.06M 147KB/s
data/re [ <=> ] 6.09M 144KB/s
data/rec [ <=> ] 6.15M 149KB/s
data/rece [ <=> ] 6.21M 150KB/s
data/recen [ <=> ] 6.25M 145KB/s
data/recens [ <=> ] 6.31M 154KB/s
data/recense [ <=> ] 6.34M 148KB/s
data/recensem [<=> ] 6.40M 153KB/s
data/recenseme [ <=> ] 6.43M 148KB/s
data/recensemen [ <=> ] 6.50M 152KB/s
data/recensement [ <=> ] 6.56M 153KB/s
data/recensement_ [ <=> ] 6.59M 148KB/s
data/recensement_l [ <=> ] 6.65M 152KB/s
data/recensement_lo [ <=> ] 6.68M 147KB/s
ata/recensement_log [ <=> ] 6.75M 152KB/s
ta/recensement_loge [ <=> ] 6.81M 149KB/s
a/recensement_logem [ <=> ] 6.84M 145KB/s
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2022-05-24 05:28:42 (187 KB/s) - ‘data/recensement_logements.csv’ saved [11293731]
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv("data/recensement_individus.csv")
df['COUPLE'] = df['COUPLE'].astype("category")
df['COUPLE'] = df['COUPLE'].cat.rename_categories({1: 'Vit en couple', 2: 'ne vit pas en couple'})
df['CS24'] = df['CS24'].astype("category")
df['CS24'] = df['CS24'].cat.rename_categories({10: 'Agriculteurs exploitants', 21: 'Artisans', 22: 'Commerçants et assimilés', 23: 'Chefs d\'entreprise de 10 salariés ou plus',
31: 'Professions libérales et assimilés', 32: 'Cadres de la fonction publique, professions intellectuelles et artistiques',
36: 'Cadres d\'entreprise', 41: 'Professions intermédiaires de l\'enseignement, de la santé, de la fonction publique et assimilés',
46: 'Professions intermédiaires administratives et commerciales des entreprises', 47: 'Techniciens',
48: 'Contremaîtres, agents de maîtrise', 51: 'Employés de la fonction publique',
54: 'Employés administratifs d\'entreprise', 55: 'Employés de commerce', 56: 'Personnels des services directs aux particuliers',
61: 'Ouvriers qualifiés', 66: 'Ouvriers non qualifiés', 69: 'Ouvriers agricoles'})
df['CS42'] = df['CS42'].astype("category")
df['CS42'] = df['CS42'].cat.rename_categories({11: 'Agriculteurs sur petites exploitations', 12: 'Agriculteurs sur moyennes exploitations', 13: 'Agriculteurs sur grandes exploitations',
21: 'Artisans', 22: 'Commerçants et assimilés' ,23: 'Chefs d\'entreprise de 10 salariés ou plus',
31: 'Professions libérales et assimilés', 33: 'Cadres de la fonction publique', 34: 'Professeurs, professions scientifiques',
35: 'Professions de l\'information, des arts et des spectacles', 37: 'Cadres administratifs et commerciaux d\'entreprise',
38: 'Ingénieurs et cadres techniques d\'entreprise', 42: 'Professeurs des écoles, instituteurs et assimilés',
43: 'Professions intermédiaires de la santé et du travail social',
44: 'Clergé, religieux', 45: 'Professions intermédiaires administratives de la fonction publique',
46: 'Professions intermédiaires administratives et commerciales des entreprises', 47: 'Techniciens',
48: 'Contremaîtres, agents de maîtrise', 52: 'Employés civils et agents de service de la fonction publique',
53: 'Policiers et militaires', 54: 'Employés administratifs d\'entreprise', 55: 'Employés de commerce',
56: 'Personnels des services directs aux particuliers', 62: 'Ouvriers qualifiés de type industriel',
63: 'Ouvriers qualifiés de type artisanal', 64: 'Chauffeurs', 65: 'Ouvriers qualifiés de la manutention, du magasinage et du transport',
67: 'Ouvriers non qualifiés de type industriel', 68: 'Ouvriers non qualifiés de type artisanal',
69: 'Ouvriers agricoles'})
df['CS8'] = df['CS8'].astype("category")
df['CS8'] = df['CS8'].cat.rename_categories({1: 'Agriculteurs exploitants', 2: 'Artisans, commerçants et chefs d\'entreprise',
3: 'Cadres et professions intellectuelles supérieures', 4 : 'Professions Intermédiaires',
5: 'Employés', 6: 'Ouvriers'})
df['CSSAL'] = df['CSSAL'].astype("category")
df['CSSAL'] = df['CSSAL'].cat.rename_categories({1: 'Manœuvre, ouvrier spécialisé', 2: 'Ouvrier qualifié ou hautement qualifié, technicien d’atelier',
3: 'Technicien (non cadre)', 4 : 'Agent de catégorie B de la fonction publique',
5: 'Agent de maîtrise, maîtrise administrative ou commerciale, VRP', 6: 'Agent de catégorie A de la fonction publique',
7: 'Ingénieur, cadre d’entreprise', 8: 'Agent de catégorie C ou D de la fonction publique',
9: 'Employé (par exemple : de bureau, de commerce, de la restauration, de maison)'})
EMPL_labels = {3: 'Artisan, commerçant, industriel, travailleur indépendant', 4: 'Stagiaire rémunéré, apprenti sous contrat',
5: 'Salarié du secteur privé à durée déterminée', 6: 'Salarié du secteur privé à durée indéterminée',
7 : 'Salarié du secteur public à durée déterminée', 8: 'Salarié du secteur public à durée indéterminé', }
df['EMPL'] = df['EMPL'].astype("category")
df['DIPL'] = df['DIPL'].astype("category")
diplomes_libelles = {1: 'Pas de scolarisation', 2: 'Aucun diplôme mais scolarisation jusqu’en primaire', 3: 'Aucun diplôme mais scolarisation jusqu’au collège',
4: 'Aucun diplôme mais scolarisation au-delà du collège', 11: 'CEP' , 12: 'BEPC, brevet élémentaire, brevet des collèges, DNB' , 13: 'CAP, BEP ou diplôme de niveau équivalent',
14: 'Bac général ou technologique, brevet supérieur, capacité en droit, DAEU, ESEU',
15: 'Bac professionnel, brevet professionnel de technicien ou d’enseignement, diplôme équivalent',
16: 'BTS, DUT, Deug, Deust, diplôme de santé ou du social niveau bac + 2, diplôme équivalent',
17: 'Licence, Licence pro, maîtrise, diplôme équivalent de niveau bac + 3 ou bac + 4',
18: 'Master, DEA, diplôme grande école niveau bac + 5, doctorat de santé',
19: 'Doctorat de recherche (hors santé)'}
#df['DIPL'] = df['DIPL'].cat.rename_categories(diplomes_libelles)
#df['CS8'] = df['CS8'].astype("category")
#df['CS8'] = df['CS8'].cat.rename_categories({ : '',: '', : '',: '', : '', })
#df['CS8'] = df['CS8'].astype("category")
#df['CS8'] = df['CS8'].cat.rename_categories({ : '',: '', : '',: '', : '', })
df.head()
| ID | IDLOG | AGEA | AGER | ANNINS | APE | CNAT | COUPLE | CPAYSN | CPAYSRA | ... | STAT | STATANT | STM | TACT | TP | TRAANT | TRANS | TYP | TYPEMPL | TYPMENR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 26769 | 76535.0 | 29 | 29 | NaN | NaN | NaN | Vit en couple | NaN | NaN | ... | 3.0 | NaN | 3 | 1.0 | 1.0 | NaN | 4.0 | 2 | 1.0 | 5.0 |
| 1 | 26772 | 43036.0 | 17 | 16 | NaN | NaN | NaN | ne vit pas en couple | NaN | NaN | ... | NaN | NaN | 6 | 3.0 | NaN | 2.0 | 5.0 | 2 | NaN | 5.0 |
| 2 | 26777 | 71698.0 | 19 | 19 | NaN | NaN | NaN | ne vit pas en couple | NaN | NaN | ... | NaN | NaN | 6 | 3.0 | NaN | 2.0 | 5.0 | 2 | NaN | 5.0 |
| 3 | 26784 | 43668.0 | 81 | 81 | 1965.0 | NaN | NaN | Vit en couple | NaN | NaN | ... | NaN | 2.0 | 1 | 5.0 | NaN | 1.0 | 2.0 | 2 | NaN | 2.0 |
| 4 | 26789 | 3381.0 | 36 | 35 | 2016.0 | 1610A | NaN | Vit en couple | NaN | NaN | ... | 3.0 | NaN | 3 | 1.0 | 1.0 | NaN | 4.0 | 2 | 1.0 | 5.0 |
5 rows × 43 columns
df.describe()
| ID | IDLOG | AGEA | AGER | ANNINS | CNAT | CPAYSN | CPAYSRA | EXER | GAD | ... | STAT | STATANT | STM | TACT | TP | TRAANT | TRANS | TYP | TYPEMPL | TYPMENR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 203144.000000 | 199929.000000 | 203144.000000 | 203144.000000 | 44082.000000 | 2412.000000 | 3137.00000 | 131.000000 | 89638.000000 | 203144.000000 | ... | 89155.000000 | 41032.000000 | 203144.000000 | 162368.000000 | 89638.000000 | 61332.000000 | 162852.000000 | 203144.000000 | 68942.000000 | 199844.000000 |
| mean | 135704.624434 | 54598.597797 | 35.574174 | 35.283617 | 1999.589651 | 432.129353 | 476.13803 | 454.458015 | 1.081428 | 3.085555 | ... | 2.755224 | 1.116494 | 3.879258 | 2.618632 | 1.114003 | 1.281729 | 3.701183 | 2.016245 | 1.252894 | 3.270726 |
| std | 78350.449683 | 31455.117174 | 21.826895 | 21.821489 | 17.898269 | 268.332958 | 76.04784 | 124.236639 | 0.328495 | 2.186296 | ... | 0.810840 | 0.379688 | 2.129446 | 1.953191 | 0.317817 | 0.449846 | 1.125026 | 0.126415 | 0.717101 | 1.215611 |
| min | 1.000000 | 2.000000 | 0.000000 | 0.000000 | 1927.000000 | 103.000000 | 127.00000 | 132.000000 | 1.000000 | 0.000000 | ... | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 2.000000 | 1.000000 | 1.000000 |
| 25% | 67790.500000 | 27344.000000 | 17.000000 | 17.000000 | 1988.000000 | 219.000000 | 501.00000 | 501.000000 | 1.000000 | 1.000000 | ... | 3.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 4.000000 | 2.000000 | 1.000000 | 3.000000 |
| 50% | 135765.000000 | 54590.000000 | 35.000000 | 34.000000 | 2006.000000 | 416.000000 | 514.00000 | 501.000000 | 1.000000 | 3.000000 | ... | 3.000000 | 1.000000 | 4.000000 | 1.000000 | 1.000000 | 1.000000 | 4.000000 | 2.000000 | 1.000000 | 3.000000 |
| 75% | 203519.250000 | 81991.000000 | 52.000000 | 51.000000 | 2015.000000 | 514.000000 | 514.00000 | 501.000000 | 1.000000 | 5.000000 | ... | 3.000000 | 1.000000 | 6.000000 | 5.000000 | 1.000000 | 2.000000 | 4.000000 | 2.000000 | 1.000000 | 4.000000 |
| max | 271406.000000 | 109025.000000 | 104.000000 | 104.000000 | 2019.000000 | 999.000000 | 514.00000 | 514.000000 | 3.000000 | 9.000000 | ... | 9.000000 | 3.000000 | 6.000000 | 7.000000 | 2.000000 | 2.000000 | 5.000000 | 3.000000 | 9.000000 | 5.000000 |
8 rows × 30 columns
from dataprep.eda import plot
plot(df)
Dataset Statistics
| Number of Variables | 43 |
|---|---|
| Number of Rows | 203144 |
| Missing Cells | 3.5524e+06 |
| Missing Cells (%) | 40.7% |
| Duplicate Rows | 0 |
| Duplicate Rows (%) | 0.0% |
| Total Size in Memory | 103.8 MB |
| Average Row Size in Memory | 535.9 B |
| Variable Types |
|
Dataset Insights
| ID is uniformly distributed | Uniform |
|---|---|
| AGEA and AGER have similar distributions | Similar Distribution |
| CNAT and CPAYSN have similar distributions | Similar Distribution |
| IDLOG has 3215 (1.58%) missing values | Missing |
| ANNINS has 159062 (78.3%) missing values | Missing |
| APE has 156869 (77.22%) missing values | Missing |
| CNAT has 200732 (98.81%) missing values | Missing |
| COUPLE has 40776 (20.07%) missing values | Missing |
| CPAYSN has 200007 (98.46%) missing values | Missing |
| CPAYSRA has 203013 (99.94%) missing values | Missing |
Dataset Insights
| CS24 has 128604 (63.31%) missing values | Missing |
|---|---|
| CS42 has 128604 (63.31%) missing values | Missing |
| CS8 has 128604 (63.31%) missing values | Missing |
| CSSAL has 140423 (69.12%) missing values | Missing |
| DIPL has 40776 (20.07%) missing values | Missing |
| EMPL has 113506 (55.87%) missing values | Missing |
| EXER has 113506 (55.87%) missing values | Missing |
| IRA has 13748 (6.77%) missing values | Missing |
| MINE has 199871 (98.39%) missing values | Missing |
| PROVRA has 13748 (6.77%) missing values | Missing |
Dataset Insights
| PROVTRA has 113506 (55.87%) missing values | Missing |
|---|---|
| RECH has 144665 (71.21%) missing values | Missing |
| SAL has 191662 (94.35%) missing values | Missing |
| SCOL has 26951 (13.27%) missing values | Missing |
| SECT10 has 113506 (55.87%) missing values | Missing |
| SECT21 has 113506 (55.87%) missing values | Missing |
| SECT5 has 113506 (55.87%) missing values | Missing |
| STAT has 113989 (56.11%) missing values | Missing |
| STATANT has 162112 (79.8%) missing values | Missing |
| TACT has 40776 (20.07%) missing values | Missing |
Dataset Insights
| TP has 113506 (55.87%) missing values | Missing |
|---|---|
| TRAANT has 141812 (69.81%) missing values | Missing |
| TRANS has 40292 (19.83%) missing values | Missing |
| TYPEMPL has 134202 (66.06%) missing values | Missing |
| TYPMENR has 3300 (1.62%) missing values | Missing |
| ANNINS is skewed | Skewed |
| CNAT is skewed | Skewed |
| CPAYSN is skewed | Skewed |
| GAD is skewed | Skewed |
| APE has a high cardinality: 369 distinct values | High Cardinality |
Dataset Insights
| MINE has constant value "1.0" | Constant |
|---|---|
| PROV has constant value "Sud" | Constant |
| APE has constant length 5 | Constant Length |
| CPAYSRA has constant length 5 | Constant Length |
| EMPL has constant length 3 | Constant Length |
| EXER has constant length 3 | Constant Length |
| GENRE has constant length 1 | Constant Length |
| ILN has constant length 1 | Constant Length |
| IRA has constant length 3 | Constant Length |
| MINE has constant length 3 | Constant Length |
Dataset Insights
| NAT has constant length 1 | Constant Length |
|---|---|
| PROV has constant length 3 | Constant Length |
| RECH has constant length 3 | Constant Length |
| SAL has constant length 3 | Constant Length |
| SCOL has constant length 3 | Constant Length |
| SECT10 has constant length 2 | Constant Length |
| SECT21 has constant length 1 | Constant Length |
| SECT5 has constant length 3 | Constant Length |
| STAT has constant length 3 | Constant Length |
| STATANT has constant length 3 | Constant Length |
Dataset Insights
| STM has constant length 1 | Constant Length |
|---|---|
| TACT has constant length 3 | Constant Length |
| TP has constant length 3 | Constant Length |
| TRAANT has constant length 3 | Constant Length |
| TRANS has constant length 3 | Constant Length |
| TYP has constant length 1 | Constant Length |
| TYPEMPL has constant length 3 | Constant Length |
| TYPMENR has constant length 3 | Constant Length |
| GAD has 28705 (14.13%) zeros | Zeros |
| GAQ has 13748 (6.77%) zeros | Zeros |
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Number of plots per page:
#df2 = df[['ID', 'DIPL', 'EMPL']]
#df2 = df2.fillna(0)
#df2.head()
#df2 = df2.pivot_table('DIPL', 'EMPL', 'ID', aggfunc="sum")
#f, ax = plt.subplots(figsize=(9, 6))
#sns.heatmap(df2, annot=True, linewidths=.5, ax=ax)
df_age = df[['AGER', 'GENRE']]
df_homme = df_age.loc[df_age['GENRE'] == 1].groupby('AGER').sum()
df_homme['AGER'] = df_homme.index
print(df_homme.shape)
print(df_homme.loc[df_homme['AGER'] == 40])
df_homme['GENRE'] = 0-df_homme['GENRE']
df_homme = df_homme.rename(columns={'GENRE': 'homme'})
df_femme = df_age.loc[df_age['GENRE'] == 2].groupby('AGER').sum()
df_femme = df_femme.rename(columns={'GENRE': 'femme'})
df_femme['AGER'] = df_femme.index
print(df_femme.shape)
df_femme.loc[df_femme['AGER'] == 40]
(101, 2)
GENRE AGER
AGER
40 1349 40
(105, 2)
| femme | AGER | |
|---|---|---|
| AGER | ||
| 40 | 3094 | 40 |
Pyramide des ages¶
sns.set(font_scale = 2)
df4 = pd.concat([df_homme, df_femme], axis=1).iloc[::-1]
figure = plt.figure(figsize=(50, 50))
bar_plot = sns.barplot(x='homme', y=df4.index, data=df4, order=df4.index, lw=0, orient='horizontal')
bar_plot = sns.barplot(x='femme', y=df4.index, data=df4, order=df4.index, lw=0, orient='horizontal')
bar_plot.set(ylabel="Age", xlabel="Nombre de personnes", title = "Pyramide des âges")
plt.plot([0,0], [0,105], linewidth=2)
#sns;barplot(data=df_age, x=)
[<matplotlib.lines.Line2D at 0x7f38a8ff9f40>]
Relation entre niveau de diplome, type d’emploi et catégorie socioprofessionnelle¶
df_metier = df[['DIPL', 'EMPL', 'CS8']].dropna()
sns.set(font_scale = 2)
fig, ax = plt.subplots(figsize=(50, 30))
df_counts = df_metier.groupby(['DIPL', 'EMPL']).size().reset_index()
df_counts.columns.values[df_counts.columns == 0] = 'count'
scale = 500*df_counts['count'].size
size = df_counts['count']/df_counts['count'].sum()*scale
#size = size.astype(float)
#sns.stripplot(x='DIPL', y='EMPL', hue='CS8', data=df_metier, ax=ax) #, size=size, sizes=(10,500)
dipl_id = [1, 2, 3, 4, 11, 12, 13, 14, 15, 16, 17, 18, 19]
dipl_lbl = ['Pas de scolarisation', 'Aucun diplôme mais scolarisation jusqu’en primaire', 'Aucun diplôme mais scolarisation jusqu’au collège',
'Aucun diplôme mais scolarisation au-delà du collège', 'CEP' , 'BEPC, brevet élémentaire, brevet des collèges, DNB' , 'CAP, BEP ou diplôme de niveau équivalent',
'Bac général ou technologique, brevet supérieur, capacité en droit, DAEU, ESEU',
'Bac professionnel, brevet professionnel de technicien ou d’enseignement, diplôme équivalent',
'BTS, DUT, Deug, Deust, diplôme de santé ou du social niveau bac + 2, diplôme équivalent',
'Licence, Licence pro, maîtrise, diplôme équivalent de niveau bac + 3 ou bac + 4',
'Master, DEA, diplôme grande école niveau bac + 5, doctorat de santé',
'Doctorat de recherche (hors santé)']
#plt.xticks(dipl_id, dipl_lbl, rotation=45, )
empl_lbl = ['Artisan, commerçant, industriel, travailleur indépendant', 'Stagiaire rémunéré, apprenti sous contrat',
'Salarié du secteur privé à durée déterminée', 'Salarié du secteur privé à durée indéterminée',
'Salarié du secteur public à durée déterminée', 'Salarié du secteur public à durée indéterminé']
from sklearn.preprocessing import OrdinalEncoder
import numpy as np
ord_enc = OrdinalEncoder()
enc_df = pd.DataFrame(ord_enc.fit_transform(df_metier), columns=list(df_metier.columns))
xnoise, ynoise = np.random.random(len(df_metier))/2, np.random.random(len(df_metier))/2
sns.scatterplot(enc_df["DIPL"]+xnoise, enc_df["EMPL"]+ynoise, alpha=0.5, hue=enc_df['CS8'], palette="hls")
plt.yticks(np.arange(0.25, len(empl_lbl)+0.25, 1), empl_lbl)
xrange = np.arange(0.25, len(dipl_lbl)+0.25, 1)
plt.xticks(xrange, dipl_lbl, rotation=90)
plt.legend(title='Categories socioprofessionnelles', loc='lower left', labels=['Agriculteurs exploitants', 'Artisans, commerçants et chefs d\'entreprise',
'Cadres et professions intellectuelles supérieures', 'Professions Intermédiaires',
'Employés', 'Ouvriers'])
/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
warnings.warn(
<matplotlib.legend.Legend at 0x7f38ade26c40>
